Integrating Reservation Mechanism into Grid Resource Management

Chen Jing, Kong Ling-fu College of Information Science & Engineering, Yanshan Uni. Qinhungdao 066004

E-mail:xychenjing@ysu.edu.cn

This work is supported by Natural Science Foundation of Hebei Province (Grant No.F2006000281)

Abstract

In this paper, an architecture framework of grid resource management supports reservation mechanism is proposed, which provides end-to-end quality of service (QoS) for users and adjusts the level of QoS. And we describe the request definition and processing of resource reservation, and design the strategies of reservation. Reservation strategies integrate resource request control, which improves the resource performance and guarantee use’s QoS. 1. Introduction

Correct execution of emerging performance-oriented grid applications often requires an end-to-end provision of high QoS. This end-to-end QoS can be achieved and guaranteed through proper configuration, reservation and allocation of corresponding resources. With the development of applications, QoS have become an important factor to appraise whether a grid succeed in implementing. The function and performance evaluation of gird services do not adopt the traditional criterion but QoS according to the goal of user’s satisfactory result. Insuring QoS of grid systems based on effective resources management that has been the research focus in many domains of computer science.

Reservation mechanism has been used to ensure the availability of necessary resources at program start time and provide an increased expectation that resources can be allocated when demanded, which improve QoS of grid system. The reservation schemes at grid level have been researched in [1]. Architecture for reservation and allocation (GARA) contains a mechanism to achieve end-to-end QoS for distributed applications with multiple resources [2]. In GARA, different kinds of resources have uniform resource reservation format, its reservation mechanism may receive specified QoS from resource manager, and

provide API to deal with requirement of reservation. GARA have obtained some applications in some extent, but the disadvantages do not support OGSA [3] and SLAs [4]. G-QoSM [5~6] advances the methods of supporting resources reservation, reservation strategies, SLA and OGSA, but pay little attention to dynamic resource management. In this paper, we put forward a new grid resource management scheme that integrates reservation mechanism, which manages resources effective and improves QoS according to reservation strategies and SLAs. 2. Request of resource reservation 2.1 Definition of reservation request

Reservation mechanism must be able to deal with three different aspects [7]: (1) specifying reservation, (2) processing reservation request, and (3) handling confirmed reservation. Therefore, we define reservation as tuple (R, M, O, T) and describe the tuple with RSL [8]. R—the set of resource vector, },,,{ 21 ni rrrrr =

},,{ capacitybwusagei StroageNetworkCPUr = M—the set of mapping from resource attribute vector to value, let Mi,j be the domain of valid values for resource vector ri , for example Mi,j={CPUusage=80%, Networkbw=10M, Storagecapacity=100M } T—the time range of resource reservation, the time format is “year/month/day/(hours)”, hours is presented as hour : minute, and T has two parameters Tstar and Tend O—the set of operation and function between ri to satisfy user’s request, and obtain the value range of Mi,j,

2.2 Processing of reservation request

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Resource reservation plan may become very time-consuming because of many constraints, the flexibility in multidimensional objective function, and the large number of resources and requests in grid system. Hence, there is a strong need for an efficient processing of reservation requests [9]. The processing of resource reservation request is showed in figure 1.

Figure 1 Processing of reservation request

(1) Registering the sets of resource vector in every VO (Virtual Organization) into RIS, the implement of RIS is based on MDS;

(2) A client submits a resource reservation request to RRS, RRS is a service in grid resource management;

(3) RRS quires RIS for resources which satisfy user’s request;

(4) RIS returns a subset of resource vector; (5) RRS matches resource vector in every VO, and

every candidate reservation resource is assigned ID value and attributes;

(6) To ensure the QoS of grid resource management, the candidate reservation resource consult with SLA;

(7) Choosing the best candidate reservation resource after consult with SLA, and confirming the basic QoS level of grid resource management when reservation resource is allowed to allocate to users;

(8) If reservation resource satisfies to user’s request and ensures the QoS level of grid system, RRS link the resource by resource attribute, or removing the candidate reservation resource, rolls back step 6 and chooses other candidate reservation resource in reservation resource queue;

(9) RRS sends a response to the client, including the successful hint information, resource vector information and reservation time restrict, ID and attribute and router information of reservation resource; or failed hint information, because the overtime or has not information which satisfies request condition or resource request control based on reservation mechanism refuses reservation request to ensure the QoS of grid resource management system. 3. Architecture integrates with reservation mechanism

We have discussed the definition and the processing of resource reservation request, and obtained the possible results from the set of resource vectors in different VOs. An architecture that supported resource reservation mechanism is proposed in this section, and the architecture is showed in figure 2.

Figure 2 Architecture that supported resource reservation mechanism

Resource description layer: it is a basic of grid application and contains three units: resource, resource abstract and resource vector. Resource unit consists of all kinds of physical and logical resources. Resources locate in different VOs and accesses strategies independently. Therefore, the characteristic of resource unit is heterogeneity, distributing and autonomous in the environment of grid application. The function of resource abstract unit is which obtains the description attributes of resources unit by the abstracting the physical and logical resources. The function of resource vector unit is to choose the specified resource attribute and form resource vector of reservation request.

Middleware: it is a layer of including basic service of GT3 (Globus Toolkit3). It is responsibility for executing service request in remote, and contains basic service toolkits, such as GRAM allocates resources request and monitor service. The function of MDS is to interact and cooperate with multi-resources, and gather dynamic and static information, and provide for users to browse the executive states of service periodic in grid system. Middleware provides basic service based on GT3, which is able to satisfy the application of service layer by expansion the GT3.

Service layer: it contains two functions, one is the resource management, and another is admission control (AC). There are resource allocation service (RAS), resource reservation service (RRS), resource information service (RIS) and resource monitoring (RM) in resource management unit, and resource request control (RRC) and QoS in AC unit. The

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function of service layer is to decide whether accepting the reservation request according to service monitoring information, QoS level and reservation strategies, and implementing the function of resource allocation which resource allocation service calls the API of GRAM.

Application layer: the main function is to send out the service request. After application layer have sent out service request, service layer is responsibility for finding the proper service and calling remote resources by middleware and dealing with the request, in order to implement the function of service request.

Grid resource portal: it provides an interface or global view for users based on Gridport technique, which consists of a portal server.

In figure 2, the implement processing of grid resource management that supported reservation mechanism is as followed.

(1) User submit service request by grid resource portal;

(2) Application layer sends service request to the correlative service unit in service layer according user’s request;

(3) Resource management unit consult with AC to decide whether accept the user’s service request according to the results of resource monitor (calling MDS in middleware, and gathering the states of resources from resource unit in resource description layer, as follows step 1 and 5) that transfer to RIS and reservation strategies, as follows step 3 and 4;

(4) If RRC is agreed, users are able to deal with RAS and RRS and query the state of resources by RIS. When RAS is chosen, RAS calls API of GRAM in middleware to implement resource allocation and execute jobs in remote resources, as follows step 2 and 7. When RRS is chosen, service layer obtains the subsets of resource vector in resource description according the monitoring result from MDS (as follows step 6) and forms the queue of candidate reservation resource. When consulting with QoS in AC of service layer, choosing the best reservation resource;

(5) If RRC is defied, adjusting the QoS level according to reservation mechanism, and put reservation resource/resource into waiting queue, until AC is agreed during the longest restrictive response time in grid resource management system, or sending failed request information to users.

4 Reservation strategies integrate with QoS

The characteristic of resource reservation strategies is which integrates resource allocation algorithm in RAS and resource request control with resource reservation strategies. From figure 2, resource reservation strategies in RRS interact with RAS and

AC. Users submit resource reservation/resource request is successful or failing, the key factor is whether RRC is able to accept. But, the influencing factor of RRC is QoS mechanism of grid resource management.

4.1 Parameters description of QoS

QoS in the environment of grid is to measure the service performance when users interact with grid resources. The performance factors include utilization ratio of resource, transaction time and service security etc. There are a lot of factors of impacting QoS of grid, because of the complexity of grid infrastructure. In our research, the parameters of QoS are defined as follows.

),,( actionlevelTQTRQoS λ= , },,{ 21 ni ttttT = is a set of jobs; },,{ 21 ni rrrrR = is a set of resource vector, },,{ netbwstoragecpui rrrr = , TR describes the needed

resources of job it , },,{ 21 miiii QQQQ = , levelTQ

describe the service level of job it , },,{ rejectacceptnegotiateaction =λ describes the

possible action, because the executive of multi-jobs compete resources and lead to the reducing QoS. The three values of actionλ that reflect of QoS of grid resource management is sent to RRC in order to decide whether user’s request is successful. Therefore, we define function )( actionf λ and range of three action values.

)( actionf λ = ωβ−kiQ , (1)

2 2 2

( , , )

( ) ( ) ( )cpu stroage netbw

cpu storage netbw

r r r

r r rβ =

+ + (2)

kiQ is chosen service level of job it in of multi executive

jobs consist of the subset of jobs, ω is a forecast value of service level that describes the absolute value of difference between actual QoS level and the corresponding service level of normalization empirical resource vector. β is the normalization result of request resource vector. 4.2 Resource reservation strategies

Suppose that we have obtained the scheduling sequence of multi jobs by improving Mapping Heuristisc algorithm, and the parameter )( actionf λ of information transfer between RRC and QoS. The resource reservation strategies are described as follows:

Initialization step:

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Assign a QoS value from },,{ 21 miiii QQQQ = for

job sequence, and form a new list of QoS level about the job sequence;

Define three queues, ready, waiting and suspend queue;

Set t=300s; // time period Processing step: Users submit reservation service request; RIS gather resource information from RM based on

MDS; Obtain utilization of correlation resource (CPU,

Storage, NetworkBW) of executing jobs (t1, t2,…… ti) at time t;

Rtotal = Rused + Rremain ; Forecast the consumption resource vectors and

service level of executing jobs set in the next time t; t=t+300; calculate parameter ω and β ; judge the state of )( actionf λ transfer to RRC; if )( actionf λ =accepted

while (QoS is able to achieve the requirement of grid system);

fetch resource vector information Rreservation from R in every time period ;

Storage Rreservation to cache of GASS; Form lists of resource vector;

end while RRS calls GASS API; Put list of Rreservation into ready queue; RRS Return resource information and time restrict

by application layer; if )( actionf λ =negotiated

Put user’s service request into waiting queue; T = deciding the request to reduce service level of

part jobs is agreed; // T is a flag, deal with request If T then do loop Reducing the QoS level of minor jobs (ti……tj); Fetch resource vector information Rreservation from

part of Rremain in every time period ; Storage Rreservation to cache of GASS;

Form lists of resource vector; until the lowest service level of minor of any jobs ti ; RRS calls GASS API; Put list of Rreservation into ready queue; RRS Return resource information and time restrict;

else return request failed; end if

if )( actionf λ =rejected Put user’s request into suspending queue;

RRS return failed information to users;

Conclusion

We have put forward a way which applies reservation mechanism to manage grid resource system effectively. Firstly, we define a tuple of describing reservation definition, and design the processing of reservation request integrating SLA and QoS, and get possible return results from reservation request. Secondly, we advance the architecture of supporting reservation mechanism and discuss the executive processing. Thirdly, designing resource reservation strategies integrate the parameters of QoS, and analyzing the relation of reservation strategies, RRC and QoS. Researching dynamic resource allocating algorithm and adjustive mechanism of QoS in the reservation strategies, and verifying the validity and performance of grid system is our future work.

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